Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation


This study proposes an umbrella deployment of swarm intelligence algorithm, like stochastic diffusion look for medical imaging applications. Once summarising the results of some previous works which shows how the algorithm assists within the identification of metastasis in bone scans and microcalcifications on mammographs, for the primary time, the employment of the algorithm in assessing the CT images of the aorta is demonstrated together with its performance in detecting the nasogastric tube in chest X-ray. The swarm intelligence algorithm presented during this study is tailored to address these explicit tasks and its functionality is investigated by running the swarms on sample CT images and X-rays whose status are determined by senior radiologists. In addition, a hybrid swarm intelligence-learning vector quantisation (LVQ) approach is proposed within the context of magnetic resonance (MR) brain image segmentation. The particle swarm optimisation is used to coach the LVQ that eliminates the iteration-dependent nature of LVQ. The proposed methodology is employed to detect the tumour regions in the abnormal MR brain pictures.

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